code: def forward(self, x, scales=None, biases=None):
      x = self.conv(x)
      if self.norm_type is not None:
          x = self.bn(x)
      # print("scales:", scales)
      # print("biases:", biases)
      if scales is not None and biases is not None:
          # print("convent forward")
          x = scales[-1] * x + biases[-1]
  
      if self.relu is not None:
          x = self.relu(x)
      return x
  
imports: from torch.nn import init
  import sys
  import torch
  import torch
  import torchvision
  from torch import nn
  from datetime import datetime
  from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments
  from fate.ml.nn.model_zoo.agg_layer.fedpass._passport_block import ConvPassportBlock
  from fate.ml.nn.model_zoo.agg_layer.fedpass.agg_layer import FedPassAggLayerHost, FedPassAggLayerGuest
  from tqdm import tqdm
  from fate.ml.nn.model_zoo.hetero_nn_model import HeteroNNModelGuest, HeteroNNModelHost
  from fate.ml.nn.hetero.hetero_nn import HeteroNNTrainerGuest, HeteroNNTrainerHost, TrainingArguments
label_desc: Use image node_id 3 for calling the ConvBlock obj's underlying member method code with example usage: obj.forward(x, scales, biases) and returns: x